Native Nanodiscs and the Convergence of Lipidomics, Metabolomics, Interactomics and Proteomics
Bibliographic record
Abstract
The omics disciplines remain largely distinct sciences due to the necessity of separating molecular classes for different assays. For example, water-soluble and lipid bilayer-bound proteins and metabolites are usually studied separately. Nonetheless, it is at the interface between these sciences where biology happens. That is, lipid-interacting proteins typically recognize and transduce signals and regulate the flow of metabolites in the cell. Technologies are emerging to converge the omics. It is now possible to separate intact membrane:protein assemblies (memteins) directly from intact cells or cell membranes. Such complexes mediate complete metabolon, receptor, channel, and transporter functions. The use of poly(styrene-co-maleic acid) (SMA) copolymers has allowed their separation in a single step without any exposure to synthetic detergents or artificial lipids. This is a critical development as these agents typically strip away biological lipids, signals, and metabolites from their physiologically-relevant positions on proteins. The resulting SMA lipid particles (SMALPs) represent native nanodiscs that are suitable for elucidation of structures and interactions that occur in vivo. Compatible tools for resolving the contained memteins include X-ray diffraction (XRD), cryo-electron microscopy (cryoEM), mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy. Recent progress shows that memteins are more representative than naked membrane proteins devoid of natural lipid and is driving the development of next generation polymers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".